Are You Making These Deadly Mistakes With Your AI Projects?

Since data is at the heart of AI, it should come as no surprise that AI and ML systems need enough good quality data to "learn". In general, a large volume of good quality data is needed, especially for supervised learning approaches, in order to properly train the AI or ML system. The exact amount of data needed may vary depending on which pattern of AI you're implementing, the algorithm you're using, and other factors such as in house versus third party data. For example, neural nets need a lot of data to be trained while decision trees or Bayesian classifiers don't need as much data to still produce high quality results. So you might think more is better, right?